Authors:
Noriko Imafuji Yasui
1
;
Shunsuke Saruwatari
1
;
Xavier Llorà
2
and
David E. Goldberg
1
Affiliations:
1
IllGAL, University of Illinois at Urbana-Champaign, United States
;
2
NCSA, University of Illinois at Urbana-Champaign, United States
Keyword(s):
Focus group discussion, facilitation, message feature map.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Business Analytics
;
Data Engineering
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Group Decision Support Systems
;
Health Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Face-to-face focus group discussion has been one of the reliable approaches for collecting variety of ideas and opinions for building marketing strategy, even though various network-based communication tools have been available. This is due to complications in facilitation of on-line discussions. The goal of this paper is to maximize the profit from on-line focus group discussions by supporting facilitators’ task. In this paper, we propose a message feature map and two metrics for measuring message feature; centrality and novelty. The message feature map plots discussion messages on centrality-novelty plane, and gives us intuitive understanding of the discussions in various aspects. Reporting experimental results by using real data collected in on-line focus group discussions, we discuss how we can utilize the message feature map for the effective facilitation.